Code chunks

The key thing for us to focus on are the code chunks, which look like this:

```{r simulate_data}
x <- rnorm(100)
y <- 2*x + rnorm(100)
```

In the midst of an otherwise plain Markdown document, you’ll have a
bit of R code that is initiated by a line like this:

```{r chunk_name}

After the code, there’ll be a line with just three backticks.

```

It’s usually best to give each code chunk a name, like simulate_data
and chunk_name above. The name is optional; if included, each code
chunk needs a distinct name. The advantage of giving each chunk a name
is that it will be easier to understand where to look for errors,
should they occur. Also, any figures that are created will be given
names based on the name of the code chunk that produced them.

When you process the R Markdown document with knitr, each of the code
chunks will be evaluated, and then the code and/or output will be
inserted (unless you suppress one or both with chunk options, described below). If
the code produces a figure, that figure will be inserted.

An R Markdown document will have often have many code chunks. They are
evaluated in order, in a single R session, and the state of the
various variables in one code chunk are preserved in future
chunks. It’s as if you’d pulled out all of the R code as a single file
(and you can do
that, using the purl command in knitr) and then
source
it into R.

Chunk options

The initial line in a code chunk may include various options. For
example, echo=FALSE indicates that the code will not be shown in the
final document (though any results/output would still be displayed).

If I’m writing a report for a collaborator, I’ll often use
include=FALSE to suppress all of the code and largely just include
figures.

For figures, you’ll want to use options like fig.width and
fig.height. For example:

```{r scatterplot, fig.width=8, fig.height=6}
plot(x,y)
```

Note that if include=FALSE, all of the code, results, and figures
will be suppressed. If include=TRUE and results="hide", the results
will be hidden but figures will still be shown. To hide the figures,
use fig.show="hide".

Global chunk options

You may be inclined to use largely the same set of chunk options
throughout a document. But it would be a pain to retype those options in every chunk. Thus, you want to set some
global chunk options at the top of your document.

For example, I might use include=FALSE or at least echo=FALSE
globally for a report to a scientific collaborator who wouldn’t want
to see all of the code. And I might want something like fig.width=12
and fig.height=6 if I generally want those sizes for my figures.

I snuck a few additional options in there: warning=FALSE and
message=FALSE suppress any R warnings or messages from being included in
the final document, and fig.path='Figs/' makes it so the figure
files get placed in the Figs subdirectory. (By default, they are not
saved at all.)

Note: the ending slash in Figs/ is important. If you used
fig.path='Figs' then the figures would go in the main directory but
with Figs as the initial part of their names.

The global chunk options become the defaults for the rest of the
document. Then if you want a particular chunk to have a different
behavior, for example, to have a different figure height, you’d
specify a different option within that chunk. For example:

In a report to a collaborator, I might use include=FALSE, echo=FALSE
as a global option, and then use include=TRUE for the chunks that
produce figures. Then the code would be suppressed throughout, and any output
would be suppressed except in the figure chunks (where I used
include=TRUE), which would produce just the figures.

Technical aside: In setting the global chunk options with
opts_chunk$set(), you’ll need to use knitr:: (or to have first
loaded the knitr package with library(knitr)). As we’ll discuss
below, we’ll use the
rmarkdown package to process
the document, first with knitr and then with
pandoc, and
rmarkdown::render() will use knitr::knit() but won’t load
the knitr package.

Package options

In addition to the chunk options, there are also
package options,
set with something like:

I was confused about this at first: I’d use opts_knit$set when I
really wanted opts_chunk$set. knitr includes a lot of options; if
you’re getting fancy you may need these package options, but initially
you’ll just be using the chunk options and, particularly, the global
chunk options defined via opts_chunk$set. So mostly ignoreopts_knit$set() in favor of opts_chunk$set().

In-line code

A key motivation for knitr is
reproducible research:
that our results are accompanied by the data and code needed to
produce them.

Thus, your report should never explicitly include numbers that are
derived from the data. Don’t write “There are 168 individuals.”
Rather, insert a bit of code that, when
evaluated, gives the number of individuals.

That’s the point of the in-line code. You’d write something like this:

There are `r nrow(my_data)` individuals.

Another example:

The estimated correlation between x and y was `r cor(x,y)`.

In R Markdown, in-line code is indicated with `r and `.
The bit of R code between them is evaluated and the result inserted.

An important point: you need to be sure that these in-line bits of
code aren’t split across lines in your document. Othewise you’ll just
see the raw code and not the result that you want.

YAML header

Insert, at the top of your R Markdown document, a bit of text like the
following:

The final document will then contain a nicely formated title, along
with the author name and date. You can include hyperlinks in there:

author: "[Karl Broman](https://kbroman.org)"

and even R code:

date: "`r Sys.Date()`"

This is called the YAML header. YAML is a
simple text-based format for specifying data, sort of like
JSON but more human-readable.

You can leave off the author and date if you want; you can leave off
the title, too. Actually, you don’t need to include any of this. But
output: html_document tells the
rmarkdown package to convert
the document to html. That’s the default, but you could also use
output: pdf_document or even output: word_document, in which case
your document will be converted to a PDF or Word .docx file,
respectively.

Rounding

I’m very particular about the rounding of results, and you should be too. If I’ve estimated
a correlation coefficient with 1000 data points, I don’t want to see
0.9032738. I want 0.90.

You could use the R function round, like this: `r round(cor(x,y), 2)`
But that would produce 0.9 instead of 0.90.

One solution is to use the sprintf function, like so:
`r sprintf("%.2f", cor(x,y))`. That’s perfectly reasonable,
right? Well, it is if you’re a C programmer.

But a problem arises if the value is -0.001. `r sprintf("%.2f", -0.001)`
will produce -0.00. I don’t like that, nor does
Hilary.

My solution to this problem is the
myround
function
in my R/broman package.

At the start of my R Markdown document, I’d include:

```{r load_packages, include=FALSE}
library(broman)
```

And then later I could write `r myround(cor(x,y), 2)`
and it would give 0.90 or 0.00 in the way that I want.

Converting R Markdown to html

Via RStudio

If you use RStudio, the simplest way to
convert an R Markdown document to html is to open the document within
RStudio. (And really, you probably want to create the document in
RStudio: click File → New File → R Markdown.)
When you open an R Markdown document in RStudio, you’ll see
a “Knit HTML” button just above the document. (It’s a particularly
cute little button, with a ball of yarn and a knitting needle.) Click
that, and another window will open, and you’ll see knitr in action,
executing each code chunk and each bit of in-line code, to compile the R
Markdown to a Markdown document. This will then be converted to html,
with a preview of the result. (The resulting .html file will be
placed in the same directory as your .Rmd file.) You can click
“Open in browser” to open the document in your web browser, or
“Publish” to publish the document to the web (where it will be
viewable by anyone).

Another a nice feature in RStudio: when you open an R Markdown
document, you’ll see a little question mark button, with links to
“Using R Markdown” and to a Markdown
Quick Reference. convenient “Markdown Quick Reference” document: a
cheat-sheet on the Markdown syntax. Like
@StrictlyStat,
I seem to visit the
Markdown website almost
every time I’m writing a Markdown document. If I used RStudio, I’d
have easier access to this information.

RStudio is especially useful when you’re first learning KnitR and R
Markdown, as it’s easy to create and view the corresponding html file,
and you have access to that Markdown Quick Reference.

Up next

At this point, I’d recommend going off and playing with R Markdown for
a while. Write your next report with R Markdown, even if it takes you
a bit longer. Write it using RStudio, where
the knitting process is easy and you have easy access to that
“Markdown Quick Reference”.